Senior Data Scientist, Spade

• 5+ years industry experience
• A Bachelor’s degree in a relevant field
• Fintech experience, specifically familiarity with payments-related data (e.g., card, debit, acquirer, and/or merchant data) and / or transaction data (e.g., card authorization and fraud prevention)
• Deep experience in machine learning, statistical algorithms, deep learning, and techniques related to entity resolution
• Fluency with Python, statistical / ML packages, and SQL queries
• Ability to create compelling narratives with data that translate complex technical concepts and findings to non-technical stakeholders, employing data science tools and visualization techniques where relevant
• Strong problem-solving skills and ability to break down complex product and technical challenges into accurately estimated milestones

Desirable

• We are looking for a product-oriented data scientist passionate about using data to solve complex problems for our customers and team members
• As a founding member of our data science team at Spade, you will have the opportunity to work on green-field data projects and play a key role in developing our product
• Ship high-impact and scalable data products that enhances Spade’s customer experience by collaborating closely with technical and non-technical functions and balancing theoretical soundness and practicality
• Build top-tier merchant databases and low-latency APIs, leveraging techniques such as large-scale entity resolution models (including probabilistic models, embeddings, and clustering models)
• Use your understanding of customer needs, internal metrics, and deep knowledge of the broader ML ecosystem to uncover fresh possibilities within our merchant and transaction data sets to bring additional value to our customers and continuously improve on Spade’s product suite
• Create advanced machine learning algorithms using Python, PySpark, Databricks, and cutting-edge technologies to streamline the preprocessing of batch data, enabling the efficient delivery and maintenance of data insights to production users
• Design and deploy end-to-end workflows and testing frameworks, orchestrating data pipelines that seamlessly integrate processed data into production systems